Natural calamities have historically impacted operational mountainous power transmission towers, including high winds and ice accumulation, which can result in pole damage or diminished load-bearing capability, compromising their structural integrity. Consequently, developing a safety state prediction model for transmission towers may efficiently monitor and evaluate potential risks, providing early warnings of structural dangers and diminishing the likelihood of bending or collapse incidents. This paper presents a safety state prediction model for transmission towers utilizing improved coati optimization-based SVM (ICOA-SVM). Initially, we optimize the coati optimization algorithm (COA) through inverse refraction learning and Levy flight strategy. Subsequently, we employ the improved coati optimization algorithm (ICOA) to refine the penalty parameters and kernel function of the support vector machine (SVM), thereby developing the safety state prediction model for the transmission tower. A finite element model is created to simulate the dynamic reaction of the transmission tower under varying wind angles and loads; ultimately, wind speed, wind angle, and ice cover thickness are utilized as inputs to the model, with the safe condition of the transmission tower being the output. The predictive outcomes indicate that the proposed ICOA-SVM model exhibits rapid convergence and high prediction accuracy, with a 62.5% reduction in root mean square error, a 59.6% decrease in average relative error, and a 75.0% decline in average absolute error compared to the conventional support vector machine. This work establishes a scientific foundation for the safety monitoring and maintenance of transmission towers, effectively identifying possible dangers and substantially decreasing the likelihood of accidents.
